ArticleFrontiers in oncology2022
Screening of possible biomarkers and therapeutic targets in kidney renal clear cell carcinoma: Evidence from bioinformatic analysis.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- scDEBGCL: a deep embedding approach based on bipartite graph contrastive learning for single-cell RNA-seq data.BMC biology · 2026Article
- The Multifunctional Roles of Aquaporins in Tumors: Focusing on Metabolism, Migration, and Regulation of the Tumor Microenvironment.International journal of molecular sciences · 2026Review
- Urinary exosomes as promising biomarkers for early kidney disease detection.American journal of clinical and experimental urology · 2025Review
- Panoramic analysis of the biological function and clinical value of SLC38A2 in human cancers: a study based on pan-cancer and single-cell analysis.Frontiers in genetics · 2025Article
- ETV4‑mediated transcriptional activation of SLC12A5 exacerbates ferroptosis resistance and glucose metabolism reprogramming in breast cancer cells.Molecular medicine reports · 2024Article
- Multiomics combined with single-cell analysis shows that mitophagy-related genes could accurately predict the prognosis of patients with clear cell renal cell carcinoma.Translational cancer research · 2024Article
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8 authors.
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Abstract
Renal cell carcinoma (RCC), as one of the most common urological malignancies, has many histologic and molecular subtypes, among which clear cell renal cell carcinoma (ccRCC) is one of the most common causes of tumor-related deaths. However, the molecular mechanism of ccRCC remains unclear. In order to identify the candidate genes that may exist in the occurrence and development of ccRCC, microarray datasets GSE6344, GSE16441, GSE36895, GSE53757 and GSE76351 had been downloaded from Gene Expression Omnibus (GEO) database. Apart from that, the differentially expressed genes (DEGs) were screened through Bioinformatics & Evolutionary Genomics. In addition, the protein-protein interaction network (PPI) was constructed, and the module analysis was performed using STRING and Cytoscape. By virtue of DAVID online database, GO/KEGG enrichment analysis of DEGs was performed. Consequently, a total of 118 DEGs were screened, including 24 up-regulated genes and 94 down-regulated genes. The plug-in MCODE of Cytoscape was adopted to analyze the most significant modules of DEGs. What's more, the genes with degree greater than 10 in DEGs were selected as the hub genes. The overall survival (OS) and disease progression free survival (DFS) of 9 hub genes were analyzed through GEPIA2 online platform. As shown by the survival analysis, SLC34A1, SLC12A3, SLC12A1, PLG, and ENO2 were closely related to the OS of ccRCC, whereas SLC34A1 and LOX were closely related to DFS. Among 11 SLC members, 6 SLC members were highly expressed in non-cancerous tissues (SLC5A2, SLC12A1, SLC12A3, SLC34A1, SLC34A2, SLC34A3). Besides, SLC12A5 and SLC12A7 were highly expressed in ccRCC. Furthermore, SLC12A1-A7, SLC34A1 and SLC34A3 were closely related to OS, whereas SLC12A2/A4/A6/A7 and SLC34A1/A3 were closely related to DFS. In addition, 5 algorithms were used to analyze hub genes, the overlapping genes were AQP2 and KCNJ1. To sum up, hub gene can help us understand the molecular mechanism of the occurrence and development of ccRCC, thereby providing a theoretical basis for the diagnosis and targeted therapy of ccRCC.
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